ABSTRACT
The majority of network connections on campus is shifting from wired to wireless. Understanding and analyzing the usage of wireless LANs can lead us beyond mere network management to the analysis of users' behavior on campus and the discovery of new insights. In 2022, campus activity has returned to levels seen before the pandemic, but the effects of the COVID-19 disaster remain. Compared to pre-pandemic years, the use of wireless LANs on campus has increased significantly due to high smartphone ownership rates and the promotion of distance learning reliant on personally owned devices (BYOD). The use of multiple devices per person has also increased, creating new issues such as congestion and IP allocation problems. To accurately understand and improve these situations, we are collecting and analyzing connection information of our wireless LAN systems. Interestingly, our analysis of wireless LAN use provides information beyond the mere discovery of technical problems. From the results, we were able to identify ways we might improve the operation of the wireless LAN, as well as information to make inferences about the time spent by students on campus, their flow lines, and their behavior patterns. In this paper, we will report on the current status of the operation of the wireless LAN at Fukuoka University and the behavioral information inferred from our analysis of wireless LAN usage. © 2023 Owner/Author.
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This paper shines the light on how the critical outbreak of Covid-19 pandemic affected restaurant consumer's purchase behaviour. With a focus on omnichannel food ordering technology the context captures the rapid development of online sales channels for restaurants in Hong Kong. Particularly focusing on integrated ordering technologies with digital payment capabilities, such as: mobile applications, website, QR code self-service ordering applications. We conduct a longitudinal field experiment between June 2020 and January 2022 in cooperation with one of the vendors for omnichannel point of sale systems (OPOSS) in Hong Kong. The result captures a panel dataset with the total number of 23 restaurants that have been opened in a continuous order throughout the pandemic. The fixed effect regression model employs an additional dataset on Covid-19 daily cases obtained from census Hong Kong SAR Government data. We apply a moderating effect based on the type of sales channel used in the restaurants. The results show that during the pandemic, some restaurants implemented omnichannel technology to sustain restaurant sales. We observe that consumers start to use omnichannel restaurant ordering technologies during the pandemic outbreak. More importantly, we find supporting evidence that after the outbreak, the omnichannel technology use behaviour among consumers remains continuous. © 2022 ACM.
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Robots' visual qualities (VQs) impact people's perception of their characteristics and affect users' behaviors and attitudes toward the robot. Recent years point toward a growing need for Socially Assistive Robots (SARs) in various contexts and functions, interacting with various users. Since SAR types have functional differences, the user experience must vary by the context of use, functionality, user characteristics, and environmental conditions. Still, SAR manufacturers often design and deploy the same robotic embodiment for diverse contexts. We argue that the visual design of SARs requires a more scientific approach considering their multiple evolving roles in future society. In this work, we define four contextual layers: the domain in which the SAR exists, the physical environment, its intended users, and the robot's role. Via an online questionnaire, we collected potential users' expectations regarding the desired characteristics and visual qualities of four different SARs: a service robot for an assisted living/retirement residence facility, a medical assistant robot for a hospital environment, a COVID-19 officer robot, and a personal assistant robot for domestic use. Results indicated that users' expectations differ regarding the robot's desired characteristics and the anticipated visual qualities for each context and use case. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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This study aims to investigate the impact on Behavioral Intention and Actual use of Mobile Learning in the Higher Education (HE). The survey was conducted at Cihan University Erbil, and the data was collected by questionnaire. 207 valid questionnaires have been analyzed by Structural equation modelling (SEM). The results indicated that performance expectancy, Effort Expectancy, Facilitating Conditions, Hedonic Motivation, and Habit had a positive and significant impact on Behavioral Intention to use Mobile learning among the students. On the other hand, Social influence and Price Value had an insignificant impact on Behavioral Intention to use Mobile learning among the students. Besides that, the current study reported that behavioral intention directly impacts user behavior (UB). At the same time, facilitating conditions and Habit had an insignificant impact on user behavior. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The development of the internet is getting faster, participating in encouraging the emergence of new and innovative information. In filtering the various information that appears, we need a recommended system to perform well for users in today's internet era. A well-performing recommendation system in question is a reliable recommendation algorithm. This algorithm is fundamental to analyzing various information, such as responses on social media based on user behavior data related to the topic of COVID. This data is crawled from tweets on social media Twitter. The data analysis algorithm obtained uses Python, which is then visualized in the form of a diagram. The processed data is user comments on Twitter, and the text data is analyzed using Python, using more than 60000 data sets taken to form visualizations and conclusions. From sentiment analysis, polarity and subjectivity data are obtained to be analyzed, which are negative, neutral, or positive. The result is show positive tweets with 29.2%, negative tweets is 13%, and 57.8% neutral tweets. Lastly, sentiment analysis can help people effectively infer vast and complex data from social media like Twitter. © 2022 IEEE.
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Purpose: The present study aims to clarify the following two research objectives: (1) the user behavior of government websites during the coronavirus disease (COVID-19) period and (2) how the government improved government's website design during the COVID-19 period. Design/methodology/approach: The authors used website analytics to examine usage patterns and behaviors of the government website via personal computer (PC) and mobile devices during the COVID-19 pandemic. In-depth interviews were conducted to understand the user experience of government website users and to gather users' opinions about how government websites should be redesigned. Findings: With the rising of the COIVID-19 pandemic, most studies expect that the use of government websites through a mobile device will grow astonishingly. The authors uncovered that the COVID-19 pandemic did not increase the use of government websites. Instead, severe declines in website usage were observed for all device users with the declines being more pronounced in mobile device users than in PC users. This is an admonitory caveat that reveals public health and pandemic prevention information announced on government websites cannot be effectively transmitted to the general public through official online platforms. Originality/value: The study highlights the gap in information behavior and usage patterns between PC and mobile device users when visiting government websites. Although mobile devices brought many new visitors, mobile devices are ineffective in retaining visitors and continuous long-term use. The results of localize experience is helpful in the improvement of government website evaluation worldwide. © 2022, Emerald Publishing Limited.
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The objective of this research is to describe and compare three different methods of generating ‘persona for lighting' to envision users' behaviour within the lighting environment. ‘Personas' are used to represent typical users, highlighting their needs, perspectives, and expectations to aid user-centric design approaches. The researchers looked for the most useful method of shaping ‘personas for lighting' to learn about users' satisfaction with the various lighting conditions to identify their needs. Method one of lighting persona development, was based on interviews with 87 users of five buildings of four different types: an office, a primary school, two university buildings, and a factory. The lighting conditions were observed and measured in all the buildings. As a result, 22 personas for lighting were created. In method two personas were generated based on pre-interviews, workshops on lighting and post-interviews with ten users along with the onsite lighting measurements. Later, due to the Covid-19 pandemic's lockdowns, an online survey on the visual lighting environment in home offices was carried out among 694 students and professionals from seven countries to create two more personas for lighting (method three). All 26 ‘personas for lighting' were generated in relation to observed lighting conditions, based on the satisfaction, preferences and needs of the users working within variously lit indoor environments. All the tested methods can be used for nearly any type of building and room, but the resulting personas are different due to the specific limitations of the methods. The created personas may help to identify future users' lighting preferences, needs and requirements and assist designers. However, to fully understand their impact on the lighting research practice they should be tested in real projects. © 2022 The Author(s)
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Many researchers have studied non-expert users' perspectives of cyber security and privacy aspects of computing devices at home, but their studies are mostly small-scale empirical studies based on online surveys and interviews and limited to one or a few specific types of devices, such as smart speakers. This paper reports our work on an online social media analysis of a large-scale Twitter dataset, covering cyber security and privacy aspects of many different types of computing devices discussed by non-expert users in the real world. We developed two new machine learning based classifiers to automatically create the Twitter dataset with 435,207 tweets posted by 337,604 non-expert users in January and February of 2019, 2020 and 2021. We analyzed the dataset using both quantitative (topic modeling and sentiment analysis) and qualitative analysis methods, leading to various previously unknown findings. For instance, we observed a sharp (more than doubled) increase of non-expert users' tweets on cyber security and privacy during the pandemic in 2021, compare to in the pre-COVID years (2019 and 2020). Our analysis revealed a diverse range of topics discussed by non-expert users, including VPNs, Wi-Fi, smartphones, laptops, smart home devices, financial security, help-seeking, and roles of different stakeholders. Overall negative sentiment was observed across almost all topics in all the three years. Our results indicate the multi-faceted nature of non-expert users' perspectives on cyber security and privacy and call for more holistic, comprehensive and nuanced research on their perspectives. © 2022
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Tiktok rose in the COVID-19 pandemic as a platform for video-sharing that ensures community connection and prevention of pessimism during social distancing. It is of special importance to thoroughly understand its potential in providing information and lockdown optimism, and its potential to cause harm on impressionable young users who occupy a great part of the Tiktok community. This research is implemented in order to learn more about young users' behaviours and their reaction towards common Tiktok contents which depict Tiktok impacts. We developed a survey using both quantitative and qualitative questions, concentrating on user habits, well-known content categories on Vietnamese Tiktok and how they may affect users' perception. 253 participants aged 16-22 in Mekong Delta, Vietnam were involved. Data revealed that young users had higher awareness of toxic contents than was assumed by previous academic works, and eager to diminish these harms. Qualitative answers provided notions about several toxic and inappropriate contents that were not previously addressed. The research also noticed a slight hint of "Tiktok prejudice"which was the fixed concept that Tiktok was negative, inspiring future extensive research. © 2022 ACM.
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The outbreak of COVID-19 burgeons newborn services on online platforms and simultaneously buoys multifarious online fraud activities. Due to the rapid technological and commercial innovation that opens up an ever-expanding set of products, the insufficient labeling data renders existing supervised or semi-supervised fraud detection models ineffective in these emerging services. However, the ever accumulated user behavioral data on online platforms might be helpful in improving the performance of fraud detection on newborn services. To this end, in this paper, we propose to pre-train user behavior sequences, which consist of orderly arranged actions, from the large-scale unlabeled data sources for online fraud detection. Recent studies illustrate accurate extraction of user intentions∼(formed by consecutive actions) in behavioral sequences can propel improvements in the performance of online fraud detection. By anatomizing the characteristic of online fraud activities, we devise a model named UB-PTM that learns knowledge of fraud activities by three agent tasks at different granularities, i.e., action, intention, and sequence levels, from large-scale unlabeled data. Extensive experiments on three downstream transaction and user-level online fraud detection tasks demonstrate that our UB-PTM is able to outperform the state-of-the-art designing for specific tasks. © 2022 ACM.
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The COVID-19 pandemic has affected human behavior drastically in various ways, including commuter patterns and traffic volumes. This paper investigates how the COVID-19 outbreak has changed the user habits and utilization patterns at public electric vehicle service equipment (EVSE). More than 7,300 charging sessions collected at 54 public Level 2 charging stations across the State of Rhode Island were analyzed using a multi-method approach comparing charging events from two time periods, before and during the pandemic. The study shows that charging behavior has changed significantly since the COVID-19 outbreak. We found that the energy consumption, charging duration, distance from home, and charging frequency decreased significantly during the pandemic. Additionally, the study discovered a relationship between the observation period and the day of the charging session. During the pandemic, charging on Sundays has become significantly more important for users than charging between Monday and Friday. We provide important insights for policymakers about how the COVID-19 pandemic has changed electric vehicle user charging behavior and demand. © 2022 IISE Annual Conference and Expo 2022. All rights reserved.
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Concept drift in stream data has been well studied in machine learning applications. In the field of recommender systems, this issue is also widely observed, as known as temporal dynamics in user behavior. Furthermore, in the context of COVID-19 pandemic related contingencies, people shift their behavior patterns extremely and tend to imitate others' opinions. The changes in user behavior may not be always rational. Thus, irrational behavior may impair the knowledge learned by the algorithm. It can cause herd effects and aggravate the popularity bias in recommender systems due to the irrational behavior of users. However, related research usually pays attention to the concept drift of individuals and overlooks the synergistic effect among users in the same social group. We conduct a study on user behavior to detect the collaborative concept drifts among users. Also, we empirically study the increase of experience of individuals can weaken herding effects. Our results suggest the CF models are highly impacted by the herd behavior and our findings could provide useful implications for the design of future recommender algorithms. © 2022 ACM.
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Limited transportation capacity and availability of PUVs are encountered daily in cities such as Manila due to the COVID-19 pandemic. Public bike-sharing like FFBS may ease these transport problems, although it is not known whether Manila is bike-friendly enough to accommodate the transport scheme. The study aims to investigate Manila through certain factors to determine the applicability of FFBS in the city. Field data collection on existing bikeways and potential bike parking, a survey on commuters in the city, and document review on legal documents was done to get an in-depth analysis regarding bike facilities, government involvement, user behavior, and expected demand for FFBS in Manila. Findings show that LOS of investigated roads and bikeways were mostly between B to C (VCR of 0.20–0.69) and at B (ave. bike speed of 15–22 kph) respectively, suitable bike parking exists along at least one side of the investigated roads, the government enabled bikeways to be set up and more are expected in the future, there is high probability of positive user behavior towards dockless bikes (Likert Scale mean above 4.0), and most of the response (75%) regarding demand leans towards agreeing to avail FFBS services. FFBS was concluded to be an applicable transport scheme in Manila. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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We conducted a lab-based eye-Tracking study to investigate how interactivity of an AI-powered fact-checking system affects user interactions, such as dwell time, attention, and mental resources involved in using the system. A within-subject experiment was conducted, where participants used an interactive and a non-interactive version of a mock AI fact-checking system, and rated their perceived correctness of COVID-19 related claims. We collected web-page interactions, eye-Tracking data, and mental workload using NASA-TLX. We found that the presence of the affordance of interactively manipulating the AI system's prediction parameters affected users' dwell times, and eye-fixations on AOIs, but not mental workload. In the interactive system, participants spent the most time evaluating claims' correctness, followed by reading news. This promising result shows a positive role of interactivity in a mixed-initiative AI-powered system. © 2022 ACM.
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This paper aims to examine the determinant factors that influence e-Wallet usage behavior, as mediated by trust and perceived risk. The research data was collected using an online questionnaire distributed using a convenience and snowball sampling technique, yielding 354 e-Wallet users. AMOS 22 software was used to analyze the data using the structural equation modeling (SEM) technique. They were tested for validity, reliability, normality, outlier, and goodness-of-fit to ensure that the model, indicators, and research data were valid. Trust, financial incentives, dispositional trust, and innovativeness had a significant positive effect on usage behavior, whereas perceived risk had no effect. © 2021 IEEE.
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Since the first confirmed case of COVID-19, in-formation was spreading in large amounts over social media platforms. Information spreading about the COVID-19 pandemic can strongly influence people's behavior. Therefore, identifying information superspreaders (or influencers) during the COVID-19 pandemic is an important step towards understanding public reactions and information dissemination. In this work, we present an analysis over a large Arabic tweets collected during the COVID-19 pandemic. The presented study construct a network from users' behaviors to identify information superspreaders during the month of March, 2020. black We employ several techniques including Centrality Metrics, HITS, PageRank, VoteRank algorithms, and the weighted correlated influence measure (WCI) to analyze the influence of information spreading, and compare the ranking of the users. The results show that the most of superspreaders were news and governments accounts © 2021 IEEE.
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The e-commerce market, which has attracted much attention in recent years, has been growing rapidly since the COVID-19 pandemic. Among these, the growth of the market for consumer-to-consumer(C2C) transactions has been remarkable. However, few studies have analyzed the C2C market during the COVID-19 pandemic, and in particular, the behavioral tendencies of the sellers are not well understood. In this study, we used C2C market transaction data to analyze the behavior of users who joined the C2C platform during the COVID-19 pandemic and identified the users who continued to use it. We found that a large number of users registered for the service to trade face masks that were in short supply in the market due to heavy demand. In addition, among the users who traded masks, only the sellers continued to use the service at a high rate, suggesting that the successful experience of selling masks is important for seller retention. These results will provide useful insights to design and implement concrete strategies for seller retention. © 2021 IEEE.
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COVID-19 has infected millions since November 2019. The virus spreads through close contact with those who are infected. People are often unaware of or lose track of their behaviors, which increase their risk of infection. Passive methods to continuously monitor and track dangerous user behaviors, maintain and update a COVID risk score can enable high risk users to take preventive measures early. At the organization or institution level, such systems can provide insights on organization-wide patterns that exacerbate disease spread. This paper presents our vision of pervasive, continuous infectious disease contact tracing, risky behavior tracking and continuous risk score calculation from contact, place visit information and reported behaviors. As a specific example, we describe the research, design and development of the android app Goatvid Trace that continuously gathers smartphone sensor data, using it to calculcate smartphone users' risk of exposure to COVID-19. Machine Learning methods for proximity detection from smart- phone Bluetooth RSSI signals are also described. GoatVid trace was deployed and evaluated on a small university community. Our evaluation study found that the mean COVID-19 risk score of college students in our study was 25.6%. Our risk score model correlated well with subject questionnaire responses with an R of 0.6166. The machine learning models for proximity detection estimated distances between two phones with a Cross Validation RMSE of 1.58766. © 2021 IEEE.
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The global Covid-19 pandemic has raised many questions about how we occupy and move in the built environment. Interior environments have been increasingly discussed in numerous studies highlighting how interior spaces play a key role in the spread of pandemics. One societal challenge is to find short-term strategies to reopen indoor venues. Most current approaches focus on an individual’s behavior (maintaining social distance, wearing face masks, and washing their hands) and government policies (confinement, curfew, quarantine, etc.). However, few studies have been conducted to understand a building’s interior where most transmission takes place. How will the utilization of existing interior spaces be improved above and beyond universally applied criteria, while minimizing the risk of disease transmission? This article presents an agent-based model that examines disease transmission risks in various “interior types” in combination with user behaviors and their mobility, as well as three types of transmission vectors (direct, airborne and via surfaces). The model also integrates numerous policy interventions, including wearing masks, hand washing, and the possibility of easily modifying the organization of spaces. Different studies at various scales were conducted both on the University of Guadalajara (UdeG) campus as well as at the MIT Media Lab to illustrate the application of this model. © 2022, Springer Nature Switzerland AG.
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The COVID-19 pandemic has affected many areas of day-to-day life, including tourism and restaurants. Many countries imposed restrictions on restaurants during the COVID19 period. Many restaurants closed, and others switched to delivery and take-out services. These restrictions affect both the catering system as a whole and smart catering systems, such as recommender systems and user experience aggregators. The main purpose of the article is to assess the impact of COVID-19 on these digital components in different countries, depending on the COVID-19 strategy. In particular, the author's contribution is as follows: (1) assessing the stability of recommendation algorithms depending on the country's COVID-19 elimination strategy, (2) identifying factors associated with changes in user behavior during the COVID-19 pandemic, (3) using these factors to improve the recommendation system, (4) answering the counter-question of whether the actual quarantine compliance can be determined using these data. As a result of the experiments, we have identified a change in the accuracy of recommendation algorithms both during and after the lockdown. We also obtained factors for changing user behavior and made assumptions about quarantine compliance in various countries using user experience data. The proposed contextual method has shown increased efficiency during the COVID-19 period. © 2021 IEEE.